# Decoding OTC Government Bond Market Liquidity: An ABM Model for Market Dynamics

arXiv: https://arxiv.org/abs/2501.16331 · HTML: https://mlsanity.com/paper/2501.16331/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 49 references that are in the map.
- First posted on arXiv 15 Dec 2024.

## Builds on (most cited first, 8 of 8)

- [Reinforcement Learning for Market Making in a Multi-agent Dealer Market](https://mlsanity.com/paper/1911.05892/) (2019)
- [Toward Policy Explanations for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2204.12568/) (2022)
- [Towards a fully RL-based Market Simulator](https://mlsanity.com/paper/2110.06829/) (2021)
- [Towards Multi-Agent Reinforcement Learning driven Over-The-Counter Market Simulations](https://mlsanity.com/paper/2210.07184/) (2022)
- [Multi-Level Mesa](https://mlsanity.com/paper/1904.08315/) (2019)
- [The Specification of Sugarscape](https://mlsanity.com/paper/1505.06012/) (2015)
- [Predicting the Behavior of Dealers in Over-The-Counter Corporate Bond Markets](https://mlsanity.com/paper/2103.09098/) (2021)
- [Growing the Simulation Ecosystem: Introducing Mesa Data to Provide Transparent, Accessible and Extensible Data Pipelines for Simulation Development](https://mlsanity.com/paper/2104.02809/) (2021)

## Built on by (most cited first, 2 of 2)

- [Shifting Power: Leveraging LLMs to Simulate Human Aversion in ABMs of Bilateral Financial Exchanges, A bond market study](https://mlsanity.com/paper/2503.00320/) (2025)
- [Multiagent Reinforcement Learning for Liquidity Games](https://mlsanity.com/paper/2601.00324/) (2026)

## Abstract (arXiv)

The over-the-counter (OTC) government bond markets are characterised by their bilateral trading structures, which pose unique challenges to understanding and ensuring market stability and liquidity. In this paper, we develop a bespoke ABM that simulates market-maker interactions within a stylised government bond market. The model focuses on the dynamics of liquidity and stability in the secondary trading of government bonds, particularly in concentrated markets like those found in Australia and the UK. Through this simulation, we test key hypotheses around improving market stability, focusing on the effects of agent diversity, business costs, and client base size. We demonstrate that greater agent diversity enhances market liquidity and that reducing the costs of market-making can improve overall market stability. The model offers insights into computational finance by simulating trading without price transparency, highlighting how micro-structural elements can affect macro-level market outcomes. This research contributes to the evolving field of computational finance by employing computational intelligence techniques to better understand the fundamental mechanics of government bond markets, providing actionable insights for both academics and practitioners.